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SALES FORECASTING
PT. Sosro, January 2010
Module 1




INTRODUCTION
Introduction
•   Find your partner                1.   What is your name?
•   Introduce your partners to the   2.   What is your place of
    class                                 employment? How long have
                                          you been with your companies?
                                          What are the areas of your
                                          responsibility?
                                     3.   What is your expectations of the
                                          course? What is one question
                                          that you hope to get answered
                                          during the class?
                                     4.   Tell us one fun thing that you
                                          like to do on weekend?
Administrative Tasks
          •   Hours
          •   Locations
          •   Emergency Phone
          •   Parking Lot
          •   Smoking Policy
          •   Attendance List
          •   Name Tents
          •   Training Manuals
Training Agenda – Day 1
09:00 – 10:30 Module 1: Introduction to sales forecasting

10:30 – 10:45 Break

10:45 – 12:00 Module 2: Indicators Affecting Sales
              Forecasting
12:00 – 1:15   Lunch

13:15 – 14:30 Module 3: Moving Average Forecasting
              Techniques
14:30 – 14:45 Break

14:45 – 15:30 Module 4: Linear Regression Forecasting
              Techniques
15:30 – 16:00 Day One Wrap Up and Preview Day Two
Training Agenda – Day 2
09:00 – 10:30   Module 5: Multiple Regression Forecasting
                Techniques
10:30 – 10:45   Break

10:45 – 12:00   Module 6: One Way Anova Forecasting
                Techniques
12:00 – 13:15   Lunch

13:15 – 14:30   Module 6: Two Way Anova Forecasting
                Techniques
14:30 – 14:45   Break

14:45 – 15:30   Module 7: Forecasting as a Strategic Business
                Tools
15:30 – 16:00   Course Summary and Wrap Up
What is Forecasting?
• Process of predicting a future event
• Underlying basis of all business decisions
   – Production
   – Inventory
   – Personnel
   – Facilities




                                               4-7
Types of Forecasts by Time Horizon




                                4-8
Short-term vs. Longer-term
        Forecasting




                             4-9
Influence of Product Life Cycle




                                  4-10
Strategy and Issues During a
                                  Product’s Life
                                 Introduction           Growth                    Maturity                Decline
                          Best period to           Practical to change    Poor time to change         Cost control
                          increase market          price or quality       image, price, or quality    critical
                          share                    image
Company Strategy/Issues




                                                                          Competitive costs
                          R&D product              Strengthen niche       become critical
                          engineering critical
                                                                          Defend market position

                                                           Drive-thru                     Fax
                                                           restaurants                    machines         3 1/2”
                                                         CD-                                               Floppy
                                                                                                           disks
                             Sales                       ROM
                                                                                                                  Station
                                                         Internet                                                 wagons
                                        Color copiers

                                  HDTV

                          Product design and       Forecasting critical    Standardization           Little product
                          development critical     Product and process     Less rapid product        differentiation
OM Strategy/Issues




                                                   reliability             changes - more minor
                          Frequent product and                             changes                   Cost minimization
                          process design           Competitive product
                          changes                  improvements and        Optimum capacity          Over capacity in the
                                                   options                 Increasing stability of   industry
                          Short production runs                            process
                                                   Increase capacity                                 Prune line to
                          High production costs                            Long production runs
                                                   Shift toward product                              eliminate items not
                          Limited models           focused                 Product improvement       returning good
                                                                           and cost cutting          margin
                          Attention to quality     Enhance distribution
                                                                                                     Reduce capacity
Module 2

INDICATORS AFFECTING SALES
FORECASTING
Types of Forecasts




                     4-13
Seven Steps in Forecasting




                             4-14
Sales over 4 Years with Trend and
                  Seasonality
                                      Seasonal peaks                  Trend component
       Sales for product or service




                                                                                Actual
                                                                                demand line



                                                              Average demand
                                                              over four years
                                                  Random
                                                  variation

                                         Year          Year       Year      Year
                                          1             2          3         4

4-15
Actual Demand, Moving Average,
   Weighted Moving Average
                               Weighted moving average

      Actual sales




                     Moving average




                        4-16
Realities of Forecasting

  • Forecasts are seldom perfect
  • Most forecasting methods assume that
    there is some underlying stability in the
    system
  • Both product family and aggregated
    product forecasts are more accurate than
    individual product forecasts


4-17
Forecasting Approach
Overview of Qualitative Methods




                              4-19
Jury of Executive Opinion
• Involves small group of high-level managers
   •   Group estimates demand by working together
• Combines managerial experience with statistical
  models
• Relatively quick
• ‘Group-think’
  disadvantage


                                                        4-20
                                                © 1995 Corel Corp.
Sales Force Composite

•   Each salesperson projects their sales
•   Combined at district & national levels
•   Sales rep’s know customers’ wants
•   Tends to be overly optimistic




                                             4-21
Delphi Method

• Iterative group process
• 3 types of people
  – Decision makers
  – Staff
  – Respondents
• Reduces ‘group-think’




                               4-22
Consumer Market Survey

• Ask customers about purchasing plans
• What consumers say, and what they actually
  do are often different
• Sometimes difficult to answer




                                           4-23
Overview of Quantitative
          Approaches
•   Naïve approach
•   Moving averages         Time-series
                            Models
•   Exponential smoothing
•   Trend projection

                            Associative
• Linear regression         models




                                          4-24
What is a Time Series?
•      Set of evenly spaced numerical data
       – Obtained by observing response variable at
         regular time periods
•      Forecast based only on past values
       – Assumes that factors influencing past and
         present will continue influence in future
•      Example
       Year:     2005    2006    2007    2008   2009
       Sales:     78.7    63.5    89.7   93.2    92.1
4-25
Time Series Components

        Trend             Cyclical




       Seasonal           Random


4-26
Trend Component
  • Persistent, overall upward or downward
    pattern
  • Due to population, technology etc.
  • Several years duration




4-27
Seasonal Component
  • Regular pattern of up & down fluctuations
  • Due to weather, customs etc.
  • Occurs within 1 year




4-28
Cyclical Component
 • Repeating up & down movements
 • Due to interactions of factors influencing
   economy
 • Usually 2-10 years duration




4-29
Random Component
  • Erratic, unsystematic, ‘residual’ fluctuations
  • Due to random variation or unforeseen
    events
       – Union strike
       – Tornado
  • Short duration &
    nonrepeating


4-30
Module 3

MOVING AVERAGE FORECASTING
TECHNIQUES
General Time Series Models
 • Any observed value in a time series is the
   product (or sum) of time series
   components
 • Multiplicative model
       – Yi = Ti · Si · Ci · Ri (if quarterly or mo. data)

 • Additive model
       – Yi = Ti + Si + Ci + Ri (if quarterly or mo. data)
4-32
Naive Approach
• Assumes demand in next period is the same as
  demand in most recent period
   •   e.g., If May sales were 48, then June sales will be 48
• Sometimes cost effective & efficient




                                                                4-33
Naïve Seasonal Model
Naïve Seasonal Model
Year   Quarter   Sales   Forecast
2004     1
         2
         3
                  500
                  350
                  250          200
                                     • Formula (1) : Yt+1 = Y1
         4        400          150
2005     5
         6
                  450
                  350
                               550
                               500
                                        – Y’24+1=Y24 , Y25=650
         7        200          250

2006
         8
         9
         10
                  300
                  350
                  200
                                50
                               400
                               400
                                        – E25=Y25 – Y’25=850-650=200
         11       150           50

2007
         12
         13
                  400
                  550
                               100
                               650   • Formula (2) : Y’t+1=Yt+(Yt-Yt-1)
         14       350          700
         15
         16
                  250
                  550
                               150
                               150      – Y24+1=Y24+(Y24-(Y24-1)
2008     17       550          850
         18
         19
                  400
                  350
                               550
                               250
                                          =650+(650-400) = 650+250 =
2009
         20
         21
         22
                  600
                  750
                  500
                               300
                               850
                               900
                                          900
         23
         24
                  400
                  650
                               250
                               300      – E25=Y25-Y’25= 850-900=-50
2010     25       850          900
         26       600         1050
         27       450          350
         28       700          300
Moving Average Method
•      MA is a series of arithmetic means
•      Used if little or no trend
•      Used often for smoothing
        – Provides overall impression of data over time
•      Equation

       MA = ∑ Demand in Previous n Periods
                         n

4-36
Moving Average Example
You’re manager that sells beverages. You
want to forecast sales (000) for 2011 using a
3-period moving average.
       2006      4
       2007      6
       2008      5
       2009      3
       2010      7


                            4-37
Moving Average Solution
       Time   Response    Moving    Moving
                 Yi        Total    Average
                           (n=3)     (n=3)
       2006    4            NA        NA
       2007    6            NA        NA
       2008    5            NA        NA
       2009    3         4+6+5=15   15/3 = 5
       2010    7
       2011        NA

4-38
Moving Average Solution
       Time   Response    Moving     Moving
                 Yi        Total     Average
                           (n=3)      (n=3)
       2006    4            NA          NA
       2007    6            NA          NA
       2008    5            NA          NA
       2009    3         4+6+5=15    15/3 = 5
       2010    7         6+5+3=14   14/3=4 2/3
       2011        NA

4-39
Moving Average Solution
       Time   Response    Moving     Moving
                 Yi        Total    Average
                           (n=3)      (n=3)
       2006    4            NA         NA
       2007    6            NA         NA
       2008    5            NA         NA
       2009    3         4+6+5=15   15/3=5.0
       2010    7         6+5+3=14   14/3=4.7
       2011        NA    5+3+7=15   15/3=5.0

4-40
Moving Average Graph

Sales
 8                     Actual
 6
                        Forecast
 4
 2
  95     96   97 98   99   00
              Year
Weighted Moving Average Method
  • Used when trend is present
       – Older data usually less important
  • Weights based on intuition
       – Often lay between 0 & 1, & sum to 1.0
  • Equation
                Σ(Weight for period n) (Demand in period n)
WMA =
                               ΣWeights




4-42
Actual Demand, Moving Average,
   Weighted Moving Average
                          Weighted moving average

      Actual sales




                     Moving average
Moving Average Technique
Moving Average Technique
   Year      t        Sales     Y't     et
     2,004        1       275
                  2       291
                  3       307
                  4       281
    2,005         5       295
                  6       268     290        (22)
                  7       252     288        (36)
                  8       279     281          (2)
    2,006         9       264     275        (11)
                 10       288     272         16
                 11       302     270         32
                 12       287     277         10
    2,007        13       290     284           6
                 14       311     286         25
                 15       277     296        (19)
                 16       245     293        (48)
    2,008        17       282     282        -
                 18       277     281          (4)
                 19       298     278         20
                 20       303     276         27
    2,009        21       310     281         29
                 22       299     294           5
                 23       285     297        (12)
                 24       250     299        (49)
    2,010        25       260     289        (29)
                 26       245     281        (36)
                 27       271     268           3
                 28       282     262         20
                 29       302     262         40
                 30       285     272         13
Double Moving Average
Double Moving Average Forecast
Time   Weekly   3 weeks   Double   Column1   Column2 Forecast (Y16 )   Column3
        sales     MA       MA       Value     Value   Y15+p = a+bp
  t       Yt       Mt      M"t        a         b        (p=1)           et
  1      654
  2      658
  3      665     659
  4      672     665
  5      673     670       665         675      5
  6      671     672       669         675      3           681          -10
  7      693     679       674         684      5           678          15
  8      694     686       679         693      7           690            4
  9      701     696       687         705      9           700            1
 10      703     699       694         704      5           714          -11
 11      702     702       699         705      3           710           -8
 12      710     705       702         708      3           708            2
 13      712     708       705         711      3           711            1
 14      711     711       708         714      3           714           -3
 15      728     717       712         722      5           717          11
 16                                                         727
Disadvantages of
       Moving Average Methods
• Increasing n makes forecast less sensitive to
  changes
• Do not forecast trend well
• Require much historical data




                                     4-48
Exponential Smoothing Method

• Form of weighted moving average
  – Weights decline exponentially
  – Most recent data weighted most
• Requires smoothing constant (α)
  – Ranges from 0 to 1
  – Subjectively chosen
• Involves little record keeping of past data
Exponential Smoothing Equations

 •     Ft = αAt - 1 + α(1-α)At - 2 + α(1- α)2·At - 3
           + α(1- α)3At - 4 + ... + α(1- α)t-1·A0
       – Ft   = Forecast value
       – At = Actual value
       – α = Smoothing constant

 •     Ft = Ft-1 + α(At-1 - Ft-1)
       – Use for computing forecast
4-50
Exponential Smoothing Example
• You’re organizing a meeting. You want to
  forecast attendance for 2011 using
  exponential smoothing
  (α = .10). The 2006 forecast was 175.
     2006 180
     2007 168
     2008 159
     2009 175
     2010 190                      © 1995 Corel Corp.


                                                        4-51
Exponential Smoothing Solution
        Ft      = Ft-1 + α(At-1 - Ft-1)
                          Forecast, tF
Time Actual
                            ( α = .10)
2006   180                      175.00 (Given)
2007   168    175.00 +
2008   159
2009   175
2010   190
2011   NA

                         4-52
Exponential Smoothing Solution
       Ft     = Ft-1 + α(At-1 - Ft-1)
                         Forecast, Ft
Time Actual
                           ( α = .10)
2006   180                      175.00 (Given)
2007   168    175.00 + .10(180 - 175.00) = 175.50
2008   159
2009   175
2010   190
2011   NA

                        4-53
Exponential Smoothing Solution
     Ft       = Ft-1 + α(At-1 - Ft-1)
                        Forecast, t
                                  F
Time Actual
                          ( α = .10)
Exponential Smoothing Graph

   Sales
  190              Actual
  180
  170              Forecast
  160
  150
  140
     06 07 08 09 10 11
           Year
              4-55
Exponential Smoothing Techniques
Exponential Smoothing
Time   Sales   Forecast   Error
  1
  2
        500
        350
                #N/A
                    350
                          #N/A
                              -
                                     • Y’4=0.6*S3 + 0.4*Y’3
  3     250         350       100
  4
  5
        400
        450
                    290
                    356
                             (110)
                              (94)   • Dumping factor (1 – α) =
  6     350         412         62
  7
  8
        200
        300
                    375
                    270
                              175
                              (30)
                                       0.4
  9     350         288       (62)
 10
 11
        200
        200
                    325
                    250
                              125
                                50
                                     • α = 0.6
 12     200         220         20
 13
 14
        550
        350
                    208
                    413
                             (342)
                                63   • Β = 0.4
 15     250         375       125
 16     550         300      (250)
 17     550         450      (100)
 18     400         510       110
 19     350         444         94
 20     600         388      (212)
 21     750         515      (235)
 22     500         656       156
 23     400         562       162
Multiplicative Seasonal Model
• Find average historical demand for each “season” by
  summing the demand for that season in each year, and
  dividing by the number of years for which you have data.
• Compute the average demand over all seasons by dividing
  the total average annual demand by the number of seasons.
• Compute a seasonal index by dividing that season’s historical
  demand (from step 1) by the average demand over all
  seasons.
• Estimate next year’s total demand
• Divide this estimate of total demand by the number of
  seasons, then multiply it by the seasonal index for that
  season. This provides the seasonal forecast.
Module 4

SIMPLE LINEAR REGRESSION
MODEL
Linear Regression Model
• Shows linear relationship between dependent
  & explanatory variables
  – Example: Sales & advertising (not time)

     Y-intercept       Slope

             ^
             Yi = a + b Xi
 Dependent                     Independent
 (response) variable           (explanatory)
                               variable
Linear Regression Techniques
Summary Output
                                                         Yt = 32.13 – 14.53 (Xt)
SUMMARY OUTPUT

        Regression Statistics
Multiple R             0.863488967
R Square               0.745613197
Adjusted R Square      0.713814846
Standard Error         2.725453111
Observations                    10

ANOVA
                         df             SS         MS        F     Significance F
Regression                      1    174.1752427 174.1752 23.44817 0.001284315
Residual                        8    59.42475728 7.428095
Total                           9           233.6

                     Coefficients Standard Error  t Stat  P-value    Lower 95%      Upper 95%   Lower 95.0% Upper 95.0%
Intercept             32.13592233    4.408587726 7.289392 8.48E-05   21.96970081    42.30214385 21.96970081 42.30214385
Price (x)            -14.53883495    3.002445334 -4.84233 0.001284    -21.4624863    -7.6151836 -21.4624863   -7.6151836
Simple Linear Regression
Simple Linear Regression

Week   Price (x)   Sales (Y)    %     Status Forecasting   %2     Status2
 1        1.3         10                              13
 2         2          6        -40%                    3   -77%
 3        1.7         5        -17%                    7   142%
 4        1.5         12       140%                   10   39%
 5        1.6         10       -17%                    9   -14%
 6        1.2         15       50%                    15   65%
 7        1.6         5        -67%                    9   -40%
 8        1.4         12       140%                   12   33%
 9         1          17       42%                    18   49%
 10       1.1         20       18%                    16    -8%
         14.4        112                            112
Linear Regression Model
       Y            Yi = a +b Xi +Error

                  Error
                          Regression line
                      ^ =a +b X
                      Yi       i

                                            X
Observed
value
4-65
Interpretation of Coefficients
  • Slope (b)
       – Estimated Y changes by b for each 1 unit
         increase in X
         • If b = 2, then sales (Y) is expected to increase by 2
           for each 1 unit increase in advertising (X)
  • Y-intercept (a)
       – Average value of Y when X = 0
         • If a = 4, then average sales (Y) is expected to be 4
           when advertising (X) is 0

4-66
Random Error Variation
  • Variation of actual Y from predicted Y
  • Measured by standard error of estimate
       – Sample standard deviation of errors
       – Denoted SY,X
  • Affects several factors
       – Parameter significance
       – Prediction accuracy


4-67
Least Squares Assumptions
  • Relationship is assumed to be linear. Plot
    the data first - if curve appears to be
    present, use curvilinear analysis.
  • Relationship is assumed to hold only within
    or slightly outside data range. Do not
    attempt to predict time periods far beyond
    the range of the data base.
  • Deviations around least squares line are
    assumed to be random.
4-68
Standard Error of the Estimate

             n
             ∑   ( yi − yi ) 2
                        ˆ
   S y,x = i =1                                      Text uses
                  n−2                                symbol Yc

               n 2           n             n
               ∑ yi    −a   ∑      yi − b ∑ x i yi
         =   i =1           i =1          i =1
                             n−2



4-69
Correlation
 • Answers: ‘how strong is the linear
   relationship between the variables?’
 • Coefficient of correlation Sample correlation
   coefficient denoted r
       – Values range from -1 to +1
       – Measures degree of association
 • Used mainly for understanding


4-70
Module 5

MULTIPLE REGRESSION MODEL
Multiple Regression Analysis
• Y = Konstanta + B1X1 + B2X2+… BnXn
Case Study
• Sosro produce 3 products (Tea, Coffee and
  Milk). How can I forecast the sales base on
  historical unit price per product?
Historical Data
Month   Sales   Tea    Coffe   Milk
  1     44439    515    541    928
  2     43936    929    692    711
  3     44464    800    710    824
  4     41533    979    675    758
  5     46343   1165   1147    635
  6     44922    651    939    901
  7     43203    847    755    580
  8     43000    942    908    589
  9     40967    630    738    682
 10     48582   1113   1175    1050
 11     45003   1086   1075    984
 12     44303    843    640    828
 13     42070    500    752    708
 14     44353    813    989    804
 15     45968   1190    823    904
 16     47781   1200   1108    1120
 17     43202    731    590    1065
 18     44074   1089    607    1132
 19     44610    786    513    839
Data Analysis
Summary Output
SUMMARY OUTPUT                                             Sales Prediction = 35,102.90+2.06 (Price Tea)
          Regression Statistics
                                                              +4.17 (Price Coffee) + 4.79 (Price Milk)
Multiple R                 0.803398744
R Square                   0.645449542
Adjusted R Square            0.57453945
Standard Error             1252.763898
Observations                         19

ANOVA
                             df              SS         MS        F     Significance F
Regression                           3    42856229.89 14285410 9.102365   0.001126532
Residual                            15    23541260.74 1569417
Total                               18    66397490.63

                        Coefficients Standard Error    t Stat    P-value    Lower 95%       Upper 95% Lower 95.0% Upper 95.0%
Intercept                35102.90045    1837.226911   19.10646   6.11E-12       31186.944    39018.8569     31186.944 39018.8569
Price Tea                2.065953296    1.664981779   1.240826   0.233727   -1.482871344    5.614777936 -1.482871344 5.614777936
Price Coffee             4.176355531    1.681252566   2.484074   0.025288    0.592850531    7.759860531 0.592850531 7.759860531
Price Milk               4.790641037    1.789316107   2.677359   0.017223    0.976804052    8.604478023 0.976804052 8.604478023
Forecast Chart
Sales   Forecast
44439     42863.99
43936     43307.07
44464     43657.66
41533     43564.31
46343     45326.54
44922     44674.48
43203     42773.37
43000     43650.19
40967     42744.04
48582     47324.03
45003     46535.27
44303      43473.5
42070     42659.16
44353     44752.07
45968     45315.47
47781     47559.16
43202     44169.51
44074     45298.81
          42879.18
Case Study
• How can I forecast the sales if qualitative
  factors (season, event, etc) involves?
Data Conversion
Year   Quarter     Sales     GDP      Unemp     Int      Q1       Q2       Q3       GDP      Unemp     Int
  2000         1      2007     2431       5.9      9.4        1        0        0     2431       5.9      9.4
  2000         2      2562     2640       5.7      9.4        0        1        0     2640       5.7      9.4
  2000         3      2385     2595       5.9      9.7        0        0        1     2595       5.9      9.7
  2000         4      2520     2701         6     11.9        0        0        0     2701         6     11.9
  2001         1      2142     2785       6.2     13.4        1        0        0     2785       6.2     13.4
  2001         2      2130     2509       7.3      9.6        0        1        0     2509       7.3      9.6
  2001         3      2190     2570       7.7     9.22        0        0        1     2570       7.7     9.22
  2001         4      2370     2667       7.4     13.6        0        0        0     2667       7.4     13.6
  2002         1      2208     2878       7.4     14.4        1        0        0     2878       7.4     14.4
  2002         2      2196     2835       7.4     15.3        0        1        0     2835       7.4     15.3
  2002         3      1758     2897       7.4     15.1        0        0        1     2897       7.4     15.1
  2002         4      1944     2744       7.4     11.8        0        0        0     2744       7.4     11.8
  2003         1      2094     2582       8.3     12.8        1        0        0     2582       8.3     12.8
  2003         2      1911     2613       8.8     12.4        0        1        0     2613       8.8     12.4
  2003         3      2031     2529       9.4      9.3        0        0        1     2529       9.4      9.3
  2003         4      2046     2544        10      7.9        0        0        0     2544        10      7.9
  2004         1      2502     2633      10.7      7.8        1        0        0     2633      10.7      7.8
  2004         2      2238     2878      10.4      8.4        0        1        0     2878      10.4      8.4
  2004         3      2394     3051       9.4      9.1        0        0        1     3051       9.4      9.1
  2004         4      2586     3274       8.5      8.8        0        0        0     3274       8.5      8.8
  2005         1      2898     3594       7.9      9.2        1        0        0     3594       7.9      9.2
  2005         2      2448     3774       7.5      9.8        0        1        0     3774       7.5      9.8
  2005         3      2460     3861       7.5     10.3        0        0        1     3861       7.5     10.3
  2005         4      2646     3919       7.2      8.8        0        0        0     3919       7.2      8.8
  2006         1      2988     4040       7.4      8.2        1        0        0     4040       7.4      8.2
  2006         2      2967     4133       7.3      7.5        0        1        0     4133       7.3      7.5
  2006         3      2439     4303       7.1      7.1        0        0        1     4303       7.1      7.1
  2006         4      2598     4393         7      7.2        0        0        0     4393         7      7.2
  2007         1      3045     4560       7.1      8.9        1        0        0     4560       7.1      8.9
  2007         2      3213     3487       7.1      7.7        0        1        0     3487       7.1      7.7
  2007         3      2685     4716       6.9      7.4        0        0        1     4716       6.9      7.4
  2007         4      3213     4796       6.8      7.4        0        0        0     4796       6.8      7.4
Data Analysis
Summary Output
SUMMARY OUTPUT

        Regression Statistics
Multiple R             0.835850806
R Square               0.698646571
Adjusted R Square      0.626321748
Standard Error         234.2782152
Observations                    32

ANOVA
                          df            SS         MS        F     Significance F
Regression                      6    3181157.823   530193 9.659845   1.56789E-05
Residual                       25    1372157.052 54886.28
Total                          31    4553314.875

                     Coefficients Standard Error    t Stat    P-value    Lower 95%     Upper 95%     Lower 95.0% Upper 95.0%
Intercept            2468.149196     565.3001581   4.366086   0.000193    1303.891736 3632.406655     1303.891736 3632.406655
Q1                   98.63689332     118.1751743   0.834667   0.411811   -144.7494321 342.0232188    -144.7494321 342.0232188
Q2                   68.23580594      118.146654   0.577552   0.568732   -175.0917808 311.5633927    -175.0917808 311.5633927
Q3                   -175.311164     117.2567064   -1.49511   0.147404   -416.8058691 66.18354178    -416.8058691 66.18354178
GDP                  0.274026559     0.069927467   3.918726    0.00061    0.130008246 0.418044872     0.130008246 0.418044872
Unemp                -47.2973846     36.21216334   -1.30612   0.203403   -121.8777304   27.2829613   -121.8777304   27.2829613
Int                  -56.5799921     21.56606503   -2.62357   0.014617   -100.9961341 -12.16385012   -100.9961341 -12.16385012
Sales forecast
Sales     Forecast
   2007     2423.114
   2562      2459.42
   2385       2177.2
   2520     2252.524
   2142      2279.95
   2130     2336.706
   2190      2112.51
   2370     2081.078
   2208     2192.292
   2196      2099.26
   1758     1884.048
   1944     2203.876
   2094     2159.108
   1911     2136.202
   2031     2016.516
   2046     2246.706
   2502     2342.302
   2238     2359.292
   2394     2170.844
   2586     2466.676
   2898     2658.676
   2448     2662.576
   2460     2414.664
   2646     2704.766
   2988      2860.98
   2967     2900.332
   2439     2735.452
   2598     2934.482
   3045      2978.07
   3213     2721.468
   2685     2841.104
   3213     3043.044
Sample Coefficient of Correlation

                        n          n      n
                      n ∑ x i yi − ∑ x i ∑ yi
       r=              i =1       i =1   i =1
             n 2  n 2  n 2  n 2 
            n ∑ x i −  ∑ x i   n ∑ y i −  ∑ y i  
             i =1      i =1    i =1       i =1  




4-83
Coefficient of Correlation Values
  Perfect                                   Perfect
 Negative                  No              Positive
Correlation            Correlation         Correlati
                                              on

   -1.0       -.5          0         +.5      +1.
                                               0
Increasing degree of            Increasing degree of
negative correlation            positive correlation


4-84
Guidelines for Selecting
             Forecasting Model
  • You want to achieve:
       – No pattern or direction in forecast error
                     ^
          • Error = (Yi - Yi) = (Actual - Forecast)
          • Seen in plots of errors over time
       – Smallest forecast error
          • Mean square error (MSE)
          • Mean absolute deviation (MAD)



4-85
Pattern of Forecast Error

    Trend Not Fully
     Accounted for
                              Desired Pattern
Error                   Error

0                        0


        Time (Years)            Time (Years)


                       4-86
Multi Regression Forecasting
Module 2

ONE WAY ANOVA
One Way Anova
Summary Out
Anova: Single Factor

SUMMARY
       Groups          Count       Sum        Average  Variance
Front                          5         45          9       2.5
Back                           4         56         14 3.333333
Middle                         3         33         11         1



ANOVA
 Source of Variation      SS       df         MS        F     P-value   F crit
Between Groups         55.66667           2 27.83333 11.38636 0.003426 4.256495
Within Groups                22           9 2.444444

Total                  77.66667          11
2 Way Anova
Two Way Anova Summary
Anova: Two-Factor Without Replication                                   17.5

      SUMMARY            Count       Sum        Average     Variance
Distrik 1                        4       26           6.5   28.33333      -11
Distrik 2                        4       59        14.75    4.916667    -2.75
Distrik 3                        4       85        21.25    10.91667     3.75
Distrik 4                        4       70          17.5          31       0
Distrik 5                        4      110          27.5   69.66667       10
                                                                        -17.5
Rep 1                            5       75          15         63.5     -2.5
Rep 2                            5       67         13.4        59.3     -4.1
Rep 3                            5      102         20.4       104.3      2.9
Rep 4                            5      106         21.2        67.7      3.7



ANOVA
 Source of Variation       SS       df      MS        F     P-value             F crit
Rows                        970.5       4 242.625 13.95065 0.000182             3.259166727
Columns                     225.8       3 75.26667 4.327743 0.027588            3.490294821
Error                       208.7      12 17.39167
                        14.44645 4.003547
Total                       1405       19



                       Distrik 2               Distrik 2
                       Rep 4 forecast          Rep 3
                       Mean            18.45        17.65
                       Lower       10.44291     9.642906
                       Upper       26.45709     25.65709
Module 4

JUDGEMENTAL FORECASTING
Multiple Regression Analysis
Any Question?
• The unique challenges associated with
  providing effective customer service to
  phone callers.
• Identify the strengths and weaknesses of
  your telephone styles and techniques.
• Identify effective telephone skills
Module 5

MANAGING THE FORECASTING
PROCESS
Learning Objectives
• Identify methods for diffusing customer anger
  or hostility
• Develop strategies for handling difficult
  customers
• Identify which verbal and non-verbal
  messages exacerbate a difficult situation and
  which diffuse a difficult situation
Strategies to Handle Difficult
           Customer Situation
1. Listen
   – Use active and reflective listening
     skills


2. Empathize
   – Putting yourself in customer shoes
   – Connect with persons feeling
       • making a statement that tells the person
         we understand the feeling
       • paraphrasing his or her words to show
         the person we understand the issue
   – Stick to what Company can and
     can’t do
Strategies to handle Difficult
           Customer Situation
3. Respond professionally
   – Use customer’s name
   – Maintain friendly manner
   – Use appropriate body
     language


4. Recognize underlying
    factors
   – Customer act for a reason
   – Negative emotion
Strategies to Handle Difficult
           Customer Situation
5. Ask question
   – Be sure to listen to everything
6. Give feedback
   – Treat the public as customer
     seeking service
   – Play tour tone of voice
7. Summarize
   – Communicate what you will
     do and when you will do it
   – Remember to under promise
     and over deliver
Limited English Speaking
• Be patient and concentrate
   – Remember, the customer is just as frustrated as you are
• Speak slowly and distinctly
   – Don’t speak so slowly that it appears to be an insult
• Be extra courteous
   – you really do care and want to help
• Avoid using slang or industry jargon
   – Use plain, simple English. Don’t use terms or phrases that will only add
     to the confusion
• Speak in normal tone of voice
   – Don’t shout. Speaking loudly won’t help
• Don’t try to listen to every word
   – Listen carefully for key words and phrases
Limited English Speaking
• Reiterate what has been said
   – Once the customer has told you what the problem is, summarize
• Don’t ask “do you understand?”
   – The customer may feel you are insulting him or her.
• Avoid humor
   – Stick to the problem. Different cultures view humor in different ways.
• Write it down
   – Use simple, short sentences.
• If you speak another language, try using it
   – The client may understand the other language better than English
• Develop a list of employee who speak foreign languages
   – Use this as a resource for helping non-English speaking customers.
• Listen to foreign language tape
Tips for Long-Winded Caller
• People will monopolize another’s time on the telephone
   – Don’t think silent or giving short answer will work
   – don’t ask questions
   – Refocus the attention
       • Stating a relevant point
   – Using “PRC” technique
       • (Paraphrase, Reflect, Close)
   – Budget time to listen
       • Budget what you can afford—but don’t tell the caller you are doing this
   – Establish mutual time limit
       • take control of the conversation before it gets too far
   – Patience: Give extra minute or two
       • Let the other party go gracefully with statements such as:“I know you are busy. I
         appreciate your help.” .“Thanks for your time. The information you have provided is very
         helpful. I’ll be back in touch as soon as. …”
Strategies to Handle
          Argumentative Customer
• Speak softly
   – the customer must be quiet in order to hear
     you.
• Ask for their opinion
   – If you give them some control by asking a
     question, they are liable to ease up.
• Take a break, don’t get drawn in
   – excuse yourself briefly, count to 10, or get a
     drink of water
• Concentrate on the points of the
  argument
   – Deal with these points one at a time.
Strategies to Handle Verbally
              Abusive Customer
• Remember, Customer isn’t angry with you
   – but at the agency, the situation, or something
     else completely unrelated
• Talk quietly
   – talk quietly so that he or she has to be quieter
     to hear you.
• Talk at normal pace
   – If you begin to talk quickly, it will only make
     matters worse
• Let the consumer know the consequences
   – When you use this language, it makes it
     impossible for me or anyone to assist you.
Strategies to Handle Threatening
              Customer
• Threat can be an attempt to
  intimidate you
• Keep calm and keep your
  responses focused on the
  issue at hand
Strategies to Handle Threatening
              Customer
• Try to avoid getting into discussion of the threat
   – Lead the conversation back to the fundamental issue in
     dispute
• Evaluate customer ability to make good on threat
  and decide what to do from there
   – Don’t overreact
   – Look for signs of drug or alcohol use
• Advice consumer of the repercussion
   – Before the threats escalate, calmly advise the customer of
     the repercussions of the threats,
• Terminate the interview
   – document the threat, warn/alert the appropriate people
     (supervisor, reception staff, etc.)
Strategies to Handle Hostile/Angry
             Customer
1. An angry customer is most   • Don’t
   likely not angry with you      –   Take the anger personally
                                  –   Blame the customer
                                  –   Avoid blame
                                  –   Dominate the conversation
Strategies to Handle Hostile/Angry
             Customer
2. Detach yourself from the
   Customer’s Hostility
   – Maintain self control
3. Hostility curve
   – Let’s wait, hear him/her out
Strategies to Handle Hostile/Angry
• Listen       Customer
   – When the customer stops talking, start giving feedback to indicate you
     heard his or her key points
• Empathize
   – you understand the situation from the customer’s perspective. Express
     empathy for the feelings expressed or demonstrated.
• Apologize
   – Apologize when the agency is at fault
• Service
   – S =Say you’re sorry. E = Expedite solutions. R = Respond to the customer. V = Victory to
     the customer. I = Implement improvements. C = Communicate results. E = Extend the
     outcome.
• Summarize
   – Clearly communicate what you will do and when you will do it
Saying “No”
• Sometimes you have to say no, but if you do it right, you can
  still get a “thank you” for your service
Saying “No”
•   Explain why it cant be done
•   Don’t quote policy
     – Don’t say, “Because it’s the law.”
•   Don’t be patronizing
     – Don’t talk down to the customer.
•   Offer alternatives when u can
     –    Try to help the customer find
         solutions to the problem.
•   Avoid making excuses
     – “I’m sorry your case hasn’t been
       processed yet
•   Eliminate negative phrases
•   Don’t mention other/similar
    complaints
Group Activity
Price for Handling Difficult Customer
Any Question?
• Methods for diffusing the anger and
  hostility of customers.
• Strategies for handling difficult customers
Module 6

SUMMARY & WRAP UP

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Sales forecasting

  • 3. Introduction • Find your partner 1. What is your name? • Introduce your partners to the 2. What is your place of class employment? How long have you been with your companies? What are the areas of your responsibility? 3. What is your expectations of the course? What is one question that you hope to get answered during the class? 4. Tell us one fun thing that you like to do on weekend?
  • 4. Administrative Tasks • Hours • Locations • Emergency Phone • Parking Lot • Smoking Policy • Attendance List • Name Tents • Training Manuals
  • 5. Training Agenda – Day 1 09:00 – 10:30 Module 1: Introduction to sales forecasting 10:30 – 10:45 Break 10:45 – 12:00 Module 2: Indicators Affecting Sales Forecasting 12:00 – 1:15 Lunch 13:15 – 14:30 Module 3: Moving Average Forecasting Techniques 14:30 – 14:45 Break 14:45 – 15:30 Module 4: Linear Regression Forecasting Techniques 15:30 – 16:00 Day One Wrap Up and Preview Day Two
  • 6. Training Agenda – Day 2 09:00 – 10:30 Module 5: Multiple Regression Forecasting Techniques 10:30 – 10:45 Break 10:45 – 12:00 Module 6: One Way Anova Forecasting Techniques 12:00 – 13:15 Lunch 13:15 – 14:30 Module 6: Two Way Anova Forecasting Techniques 14:30 – 14:45 Break 14:45 – 15:30 Module 7: Forecasting as a Strategic Business Tools 15:30 – 16:00 Course Summary and Wrap Up
  • 7. What is Forecasting? • Process of predicting a future event • Underlying basis of all business decisions – Production – Inventory – Personnel – Facilities 4-7
  • 8. Types of Forecasts by Time Horizon 4-8
  • 9. Short-term vs. Longer-term Forecasting 4-9
  • 10. Influence of Product Life Cycle 4-10
  • 11. Strategy and Issues During a Product’s Life Introduction Growth Maturity Decline Best period to Practical to change Poor time to change Cost control increase market price or quality image, price, or quality critical share image Company Strategy/Issues Competitive costs R&D product Strengthen niche become critical engineering critical Defend market position Drive-thru Fax restaurants machines 3 1/2” CD- Floppy disks Sales ROM Station Internet wagons Color copiers HDTV Product design and Forecasting critical Standardization Little product development critical Product and process Less rapid product differentiation OM Strategy/Issues reliability changes - more minor Frequent product and changes Cost minimization process design Competitive product changes improvements and Optimum capacity Over capacity in the options Increasing stability of industry Short production runs process Increase capacity Prune line to High production costs Long production runs Shift toward product eliminate items not Limited models focused Product improvement returning good and cost cutting margin Attention to quality Enhance distribution Reduce capacity
  • 12. Module 2 INDICATORS AFFECTING SALES FORECASTING
  • 14. Seven Steps in Forecasting 4-14
  • 15. Sales over 4 Years with Trend and Seasonality Seasonal peaks Trend component Sales for product or service Actual demand line Average demand over four years Random variation Year Year Year Year 1 2 3 4 4-15
  • 16. Actual Demand, Moving Average, Weighted Moving Average Weighted moving average Actual sales Moving average 4-16
  • 17. Realities of Forecasting • Forecasts are seldom perfect • Most forecasting methods assume that there is some underlying stability in the system • Both product family and aggregated product forecasts are more accurate than individual product forecasts 4-17
  • 19. Overview of Qualitative Methods 4-19
  • 20. Jury of Executive Opinion • Involves small group of high-level managers • Group estimates demand by working together • Combines managerial experience with statistical models • Relatively quick • ‘Group-think’ disadvantage 4-20 © 1995 Corel Corp.
  • 21. Sales Force Composite • Each salesperson projects their sales • Combined at district & national levels • Sales rep’s know customers’ wants • Tends to be overly optimistic 4-21
  • 22. Delphi Method • Iterative group process • 3 types of people – Decision makers – Staff – Respondents • Reduces ‘group-think’ 4-22
  • 23. Consumer Market Survey • Ask customers about purchasing plans • What consumers say, and what they actually do are often different • Sometimes difficult to answer 4-23
  • 24. Overview of Quantitative Approaches • Naïve approach • Moving averages Time-series Models • Exponential smoothing • Trend projection Associative • Linear regression models 4-24
  • 25. What is a Time Series? • Set of evenly spaced numerical data – Obtained by observing response variable at regular time periods • Forecast based only on past values – Assumes that factors influencing past and present will continue influence in future • Example Year: 2005 2006 2007 2008 2009 Sales: 78.7 63.5 89.7 93.2 92.1 4-25
  • 26. Time Series Components Trend Cyclical Seasonal Random 4-26
  • 27. Trend Component • Persistent, overall upward or downward pattern • Due to population, technology etc. • Several years duration 4-27
  • 28. Seasonal Component • Regular pattern of up & down fluctuations • Due to weather, customs etc. • Occurs within 1 year 4-28
  • 29. Cyclical Component • Repeating up & down movements • Due to interactions of factors influencing economy • Usually 2-10 years duration 4-29
  • 30. Random Component • Erratic, unsystematic, ‘residual’ fluctuations • Due to random variation or unforeseen events – Union strike – Tornado • Short duration & nonrepeating 4-30
  • 31. Module 3 MOVING AVERAGE FORECASTING TECHNIQUES
  • 32. General Time Series Models • Any observed value in a time series is the product (or sum) of time series components • Multiplicative model – Yi = Ti · Si · Ci · Ri (if quarterly or mo. data) • Additive model – Yi = Ti + Si + Ci + Ri (if quarterly or mo. data) 4-32
  • 33. Naive Approach • Assumes demand in next period is the same as demand in most recent period • e.g., If May sales were 48, then June sales will be 48 • Sometimes cost effective & efficient 4-33
  • 35. Naïve Seasonal Model Year Quarter Sales Forecast 2004 1 2 3 500 350 250 200 • Formula (1) : Yt+1 = Y1 4 400 150 2005 5 6 450 350 550 500 – Y’24+1=Y24 , Y25=650 7 200 250 2006 8 9 10 300 350 200 50 400 400 – E25=Y25 – Y’25=850-650=200 11 150 50 2007 12 13 400 550 100 650 • Formula (2) : Y’t+1=Yt+(Yt-Yt-1) 14 350 700 15 16 250 550 150 150 – Y24+1=Y24+(Y24-(Y24-1) 2008 17 550 850 18 19 400 350 550 250 =650+(650-400) = 650+250 = 2009 20 21 22 600 750 500 300 850 900 900 23 24 400 650 250 300 – E25=Y25-Y’25= 850-900=-50 2010 25 850 900 26 600 1050 27 450 350 28 700 300
  • 36. Moving Average Method • MA is a series of arithmetic means • Used if little or no trend • Used often for smoothing – Provides overall impression of data over time • Equation MA = ∑ Demand in Previous n Periods n 4-36
  • 37. Moving Average Example You’re manager that sells beverages. You want to forecast sales (000) for 2011 using a 3-period moving average. 2006 4 2007 6 2008 5 2009 3 2010 7 4-37
  • 38. Moving Average Solution Time Response Moving Moving Yi Total Average (n=3) (n=3) 2006 4 NA NA 2007 6 NA NA 2008 5 NA NA 2009 3 4+6+5=15 15/3 = 5 2010 7 2011 NA 4-38
  • 39. Moving Average Solution Time Response Moving Moving Yi Total Average (n=3) (n=3) 2006 4 NA NA 2007 6 NA NA 2008 5 NA NA 2009 3 4+6+5=15 15/3 = 5 2010 7 6+5+3=14 14/3=4 2/3 2011 NA 4-39
  • 40. Moving Average Solution Time Response Moving Moving Yi Total Average (n=3) (n=3) 2006 4 NA NA 2007 6 NA NA 2008 5 NA NA 2009 3 4+6+5=15 15/3=5.0 2010 7 6+5+3=14 14/3=4.7 2011 NA 5+3+7=15 15/3=5.0 4-40
  • 41. Moving Average Graph Sales 8 Actual 6 Forecast 4 2 95 96 97 98 99 00 Year
  • 42. Weighted Moving Average Method • Used when trend is present – Older data usually less important • Weights based on intuition – Often lay between 0 & 1, & sum to 1.0 • Equation Σ(Weight for period n) (Demand in period n) WMA = ΣWeights 4-42
  • 43. Actual Demand, Moving Average, Weighted Moving Average Weighted moving average Actual sales Moving average
  • 45. Moving Average Technique Year t Sales Y't et 2,004 1 275 2 291 3 307 4 281 2,005 5 295 6 268 290 (22) 7 252 288 (36) 8 279 281 (2) 2,006 9 264 275 (11) 10 288 272 16 11 302 270 32 12 287 277 10 2,007 13 290 284 6 14 311 286 25 15 277 296 (19) 16 245 293 (48) 2,008 17 282 282 - 18 277 281 (4) 19 298 278 20 20 303 276 27 2,009 21 310 281 29 22 299 294 5 23 285 297 (12) 24 250 299 (49) 2,010 25 260 289 (29) 26 245 281 (36) 27 271 268 3 28 282 262 20 29 302 262 40 30 285 272 13
  • 47. Double Moving Average Forecast Time Weekly 3 weeks Double Column1 Column2 Forecast (Y16 ) Column3 sales MA MA Value Value Y15+p = a+bp t Yt Mt M"t a b (p=1) et 1 654 2 658 3 665 659 4 672 665 5 673 670 665 675 5 6 671 672 669 675 3 681 -10 7 693 679 674 684 5 678 15 8 694 686 679 693 7 690 4 9 701 696 687 705 9 700 1 10 703 699 694 704 5 714 -11 11 702 702 699 705 3 710 -8 12 710 705 702 708 3 708 2 13 712 708 705 711 3 711 1 14 711 711 708 714 3 714 -3 15 728 717 712 722 5 717 11 16 727
  • 48. Disadvantages of Moving Average Methods • Increasing n makes forecast less sensitive to changes • Do not forecast trend well • Require much historical data 4-48
  • 49. Exponential Smoothing Method • Form of weighted moving average – Weights decline exponentially – Most recent data weighted most • Requires smoothing constant (α) – Ranges from 0 to 1 – Subjectively chosen • Involves little record keeping of past data
  • 50. Exponential Smoothing Equations • Ft = αAt - 1 + α(1-α)At - 2 + α(1- α)2·At - 3 + α(1- α)3At - 4 + ... + α(1- α)t-1·A0 – Ft = Forecast value – At = Actual value – α = Smoothing constant • Ft = Ft-1 + α(At-1 - Ft-1) – Use for computing forecast 4-50
  • 51. Exponential Smoothing Example • You’re organizing a meeting. You want to forecast attendance for 2011 using exponential smoothing (α = .10). The 2006 forecast was 175. 2006 180 2007 168 2008 159 2009 175 2010 190 © 1995 Corel Corp. 4-51
  • 52. Exponential Smoothing Solution Ft = Ft-1 + α(At-1 - Ft-1) Forecast, tF Time Actual ( α = .10) 2006 180 175.00 (Given) 2007 168 175.00 + 2008 159 2009 175 2010 190 2011 NA 4-52
  • 53. Exponential Smoothing Solution Ft = Ft-1 + α(At-1 - Ft-1) Forecast, Ft Time Actual ( α = .10) 2006 180 175.00 (Given) 2007 168 175.00 + .10(180 - 175.00) = 175.50 2008 159 2009 175 2010 190 2011 NA 4-53
  • 54. Exponential Smoothing Solution Ft = Ft-1 + α(At-1 - Ft-1) Forecast, t F Time Actual ( α = .10)
  • 55. Exponential Smoothing Graph Sales 190 Actual 180 170 Forecast 160 150 140 06 07 08 09 10 11 Year 4-55
  • 57. Exponential Smoothing Time Sales Forecast Error 1 2 500 350 #N/A 350 #N/A - • Y’4=0.6*S3 + 0.4*Y’3 3 250 350 100 4 5 400 450 290 356 (110) (94) • Dumping factor (1 – α) = 6 350 412 62 7 8 200 300 375 270 175 (30) 0.4 9 350 288 (62) 10 11 200 200 325 250 125 50 • α = 0.6 12 200 220 20 13 14 550 350 208 413 (342) 63 • Β = 0.4 15 250 375 125 16 550 300 (250) 17 550 450 (100) 18 400 510 110 19 350 444 94 20 600 388 (212) 21 750 515 (235) 22 500 656 156 23 400 562 162
  • 58. Multiplicative Seasonal Model • Find average historical demand for each “season” by summing the demand for that season in each year, and dividing by the number of years for which you have data. • Compute the average demand over all seasons by dividing the total average annual demand by the number of seasons. • Compute a seasonal index by dividing that season’s historical demand (from step 1) by the average demand over all seasons. • Estimate next year’s total demand • Divide this estimate of total demand by the number of seasons, then multiply it by the seasonal index for that season. This provides the seasonal forecast.
  • 59. Module 4 SIMPLE LINEAR REGRESSION MODEL
  • 60. Linear Regression Model • Shows linear relationship between dependent & explanatory variables – Example: Sales & advertising (not time) Y-intercept Slope ^ Yi = a + b Xi Dependent Independent (response) variable (explanatory) variable
  • 62. Summary Output Yt = 32.13 – 14.53 (Xt) SUMMARY OUTPUT Regression Statistics Multiple R 0.863488967 R Square 0.745613197 Adjusted R Square 0.713814846 Standard Error 2.725453111 Observations 10 ANOVA df SS MS F Significance F Regression 1 174.1752427 174.1752 23.44817 0.001284315 Residual 8 59.42475728 7.428095 Total 9 233.6 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 32.13592233 4.408587726 7.289392 8.48E-05 21.96970081 42.30214385 21.96970081 42.30214385 Price (x) -14.53883495 3.002445334 -4.84233 0.001284 -21.4624863 -7.6151836 -21.4624863 -7.6151836
  • 64. Simple Linear Regression Week Price (x) Sales (Y) % Status Forecasting %2 Status2 1 1.3 10 13 2 2 6 -40% 3 -77% 3 1.7 5 -17% 7 142% 4 1.5 12 140% 10 39% 5 1.6 10 -17% 9 -14% 6 1.2 15 50% 15 65% 7 1.6 5 -67% 9 -40% 8 1.4 12 140% 12 33% 9 1 17 42% 18 49% 10 1.1 20 18% 16 -8% 14.4 112 112
  • 65. Linear Regression Model Y Yi = a +b Xi +Error Error Regression line ^ =a +b X Yi i X Observed value 4-65
  • 66. Interpretation of Coefficients • Slope (b) – Estimated Y changes by b for each 1 unit increase in X • If b = 2, then sales (Y) is expected to increase by 2 for each 1 unit increase in advertising (X) • Y-intercept (a) – Average value of Y when X = 0 • If a = 4, then average sales (Y) is expected to be 4 when advertising (X) is 0 4-66
  • 67. Random Error Variation • Variation of actual Y from predicted Y • Measured by standard error of estimate – Sample standard deviation of errors – Denoted SY,X • Affects several factors – Parameter significance – Prediction accuracy 4-67
  • 68. Least Squares Assumptions • Relationship is assumed to be linear. Plot the data first - if curve appears to be present, use curvilinear analysis. • Relationship is assumed to hold only within or slightly outside data range. Do not attempt to predict time periods far beyond the range of the data base. • Deviations around least squares line are assumed to be random. 4-68
  • 69. Standard Error of the Estimate n ∑ ( yi − yi ) 2 ˆ S y,x = i =1 Text uses n−2 symbol Yc n 2 n n ∑ yi −a ∑ yi − b ∑ x i yi = i =1 i =1 i =1 n−2 4-69
  • 70. Correlation • Answers: ‘how strong is the linear relationship between the variables?’ • Coefficient of correlation Sample correlation coefficient denoted r – Values range from -1 to +1 – Measures degree of association • Used mainly for understanding 4-70
  • 72. Multiple Regression Analysis • Y = Konstanta + B1X1 + B2X2+… BnXn
  • 73. Case Study • Sosro produce 3 products (Tea, Coffee and Milk). How can I forecast the sales base on historical unit price per product?
  • 74. Historical Data Month Sales Tea Coffe Milk 1 44439 515 541 928 2 43936 929 692 711 3 44464 800 710 824 4 41533 979 675 758 5 46343 1165 1147 635 6 44922 651 939 901 7 43203 847 755 580 8 43000 942 908 589 9 40967 630 738 682 10 48582 1113 1175 1050 11 45003 1086 1075 984 12 44303 843 640 828 13 42070 500 752 708 14 44353 813 989 804 15 45968 1190 823 904 16 47781 1200 1108 1120 17 43202 731 590 1065 18 44074 1089 607 1132 19 44610 786 513 839
  • 76. Summary Output SUMMARY OUTPUT Sales Prediction = 35,102.90+2.06 (Price Tea) Regression Statistics +4.17 (Price Coffee) + 4.79 (Price Milk) Multiple R 0.803398744 R Square 0.645449542 Adjusted R Square 0.57453945 Standard Error 1252.763898 Observations 19 ANOVA df SS MS F Significance F Regression 3 42856229.89 14285410 9.102365 0.001126532 Residual 15 23541260.74 1569417 Total 18 66397490.63 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 35102.90045 1837.226911 19.10646 6.11E-12 31186.944 39018.8569 31186.944 39018.8569 Price Tea 2.065953296 1.664981779 1.240826 0.233727 -1.482871344 5.614777936 -1.482871344 5.614777936 Price Coffee 4.176355531 1.681252566 2.484074 0.025288 0.592850531 7.759860531 0.592850531 7.759860531 Price Milk 4.790641037 1.789316107 2.677359 0.017223 0.976804052 8.604478023 0.976804052 8.604478023
  • 77. Forecast Chart Sales Forecast 44439 42863.99 43936 43307.07 44464 43657.66 41533 43564.31 46343 45326.54 44922 44674.48 43203 42773.37 43000 43650.19 40967 42744.04 48582 47324.03 45003 46535.27 44303 43473.5 42070 42659.16 44353 44752.07 45968 45315.47 47781 47559.16 43202 44169.51 44074 45298.81 42879.18
  • 78. Case Study • How can I forecast the sales if qualitative factors (season, event, etc) involves?
  • 79. Data Conversion Year Quarter Sales GDP Unemp Int Q1 Q2 Q3 GDP Unemp Int 2000 1 2007 2431 5.9 9.4 1 0 0 2431 5.9 9.4 2000 2 2562 2640 5.7 9.4 0 1 0 2640 5.7 9.4 2000 3 2385 2595 5.9 9.7 0 0 1 2595 5.9 9.7 2000 4 2520 2701 6 11.9 0 0 0 2701 6 11.9 2001 1 2142 2785 6.2 13.4 1 0 0 2785 6.2 13.4 2001 2 2130 2509 7.3 9.6 0 1 0 2509 7.3 9.6 2001 3 2190 2570 7.7 9.22 0 0 1 2570 7.7 9.22 2001 4 2370 2667 7.4 13.6 0 0 0 2667 7.4 13.6 2002 1 2208 2878 7.4 14.4 1 0 0 2878 7.4 14.4 2002 2 2196 2835 7.4 15.3 0 1 0 2835 7.4 15.3 2002 3 1758 2897 7.4 15.1 0 0 1 2897 7.4 15.1 2002 4 1944 2744 7.4 11.8 0 0 0 2744 7.4 11.8 2003 1 2094 2582 8.3 12.8 1 0 0 2582 8.3 12.8 2003 2 1911 2613 8.8 12.4 0 1 0 2613 8.8 12.4 2003 3 2031 2529 9.4 9.3 0 0 1 2529 9.4 9.3 2003 4 2046 2544 10 7.9 0 0 0 2544 10 7.9 2004 1 2502 2633 10.7 7.8 1 0 0 2633 10.7 7.8 2004 2 2238 2878 10.4 8.4 0 1 0 2878 10.4 8.4 2004 3 2394 3051 9.4 9.1 0 0 1 3051 9.4 9.1 2004 4 2586 3274 8.5 8.8 0 0 0 3274 8.5 8.8 2005 1 2898 3594 7.9 9.2 1 0 0 3594 7.9 9.2 2005 2 2448 3774 7.5 9.8 0 1 0 3774 7.5 9.8 2005 3 2460 3861 7.5 10.3 0 0 1 3861 7.5 10.3 2005 4 2646 3919 7.2 8.8 0 0 0 3919 7.2 8.8 2006 1 2988 4040 7.4 8.2 1 0 0 4040 7.4 8.2 2006 2 2967 4133 7.3 7.5 0 1 0 4133 7.3 7.5 2006 3 2439 4303 7.1 7.1 0 0 1 4303 7.1 7.1 2006 4 2598 4393 7 7.2 0 0 0 4393 7 7.2 2007 1 3045 4560 7.1 8.9 1 0 0 4560 7.1 8.9 2007 2 3213 3487 7.1 7.7 0 1 0 3487 7.1 7.7 2007 3 2685 4716 6.9 7.4 0 0 1 4716 6.9 7.4 2007 4 3213 4796 6.8 7.4 0 0 0 4796 6.8 7.4
  • 81. Summary Output SUMMARY OUTPUT Regression Statistics Multiple R 0.835850806 R Square 0.698646571 Adjusted R Square 0.626321748 Standard Error 234.2782152 Observations 32 ANOVA df SS MS F Significance F Regression 6 3181157.823 530193 9.659845 1.56789E-05 Residual 25 1372157.052 54886.28 Total 31 4553314.875 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2468.149196 565.3001581 4.366086 0.000193 1303.891736 3632.406655 1303.891736 3632.406655 Q1 98.63689332 118.1751743 0.834667 0.411811 -144.7494321 342.0232188 -144.7494321 342.0232188 Q2 68.23580594 118.146654 0.577552 0.568732 -175.0917808 311.5633927 -175.0917808 311.5633927 Q3 -175.311164 117.2567064 -1.49511 0.147404 -416.8058691 66.18354178 -416.8058691 66.18354178 GDP 0.274026559 0.069927467 3.918726 0.00061 0.130008246 0.418044872 0.130008246 0.418044872 Unemp -47.2973846 36.21216334 -1.30612 0.203403 -121.8777304 27.2829613 -121.8777304 27.2829613 Int -56.5799921 21.56606503 -2.62357 0.014617 -100.9961341 -12.16385012 -100.9961341 -12.16385012
  • 82. Sales forecast Sales Forecast 2007 2423.114 2562 2459.42 2385 2177.2 2520 2252.524 2142 2279.95 2130 2336.706 2190 2112.51 2370 2081.078 2208 2192.292 2196 2099.26 1758 1884.048 1944 2203.876 2094 2159.108 1911 2136.202 2031 2016.516 2046 2246.706 2502 2342.302 2238 2359.292 2394 2170.844 2586 2466.676 2898 2658.676 2448 2662.576 2460 2414.664 2646 2704.766 2988 2860.98 2967 2900.332 2439 2735.452 2598 2934.482 3045 2978.07 3213 2721.468 2685 2841.104 3213 3043.044
  • 83. Sample Coefficient of Correlation n n n n ∑ x i yi − ∑ x i ∑ yi r= i =1 i =1 i =1  n 2  n 2  n 2  n 2  n ∑ x i −  ∑ x i   n ∑ y i −  ∑ y i    i =1  i =1    i =1  i =1   4-83
  • 84. Coefficient of Correlation Values Perfect Perfect Negative No Positive Correlation Correlation Correlati on -1.0 -.5 0 +.5 +1. 0 Increasing degree of Increasing degree of negative correlation positive correlation 4-84
  • 85. Guidelines for Selecting Forecasting Model • You want to achieve: – No pattern or direction in forecast error ^ • Error = (Yi - Yi) = (Actual - Forecast) • Seen in plots of errors over time – Smallest forecast error • Mean square error (MSE) • Mean absolute deviation (MAD) 4-85
  • 86. Pattern of Forecast Error Trend Not Fully Accounted for Desired Pattern Error Error 0 0 Time (Years) Time (Years) 4-86
  • 89.
  • 91. Summary Out Anova: Single Factor SUMMARY Groups Count Sum Average Variance Front 5 45 9 2.5 Back 4 56 14 3.333333 Middle 3 33 11 1 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 55.66667 2 27.83333 11.38636 0.003426 4.256495 Within Groups 22 9 2.444444 Total 77.66667 11
  • 93. Two Way Anova Summary Anova: Two-Factor Without Replication 17.5 SUMMARY Count Sum Average Variance Distrik 1 4 26 6.5 28.33333 -11 Distrik 2 4 59 14.75 4.916667 -2.75 Distrik 3 4 85 21.25 10.91667 3.75 Distrik 4 4 70 17.5 31 0 Distrik 5 4 110 27.5 69.66667 10 -17.5 Rep 1 5 75 15 63.5 -2.5 Rep 2 5 67 13.4 59.3 -4.1 Rep 3 5 102 20.4 104.3 2.9 Rep 4 5 106 21.2 67.7 3.7 ANOVA Source of Variation SS df MS F P-value F crit Rows 970.5 4 242.625 13.95065 0.000182 3.259166727 Columns 225.8 3 75.26667 4.327743 0.027588 3.490294821 Error 208.7 12 17.39167 14.44645 4.003547 Total 1405 19 Distrik 2 Distrik 2 Rep 4 forecast Rep 3 Mean 18.45 17.65 Lower 10.44291 9.642906 Upper 26.45709 25.65709
  • 96. Any Question? • The unique challenges associated with providing effective customer service to phone callers. • Identify the strengths and weaknesses of your telephone styles and techniques. • Identify effective telephone skills
  • 97. Module 5 MANAGING THE FORECASTING PROCESS
  • 98. Learning Objectives • Identify methods for diffusing customer anger or hostility • Develop strategies for handling difficult customers • Identify which verbal and non-verbal messages exacerbate a difficult situation and which diffuse a difficult situation
  • 99. Strategies to Handle Difficult Customer Situation 1. Listen – Use active and reflective listening skills 2. Empathize – Putting yourself in customer shoes – Connect with persons feeling • making a statement that tells the person we understand the feeling • paraphrasing his or her words to show the person we understand the issue – Stick to what Company can and can’t do
  • 100. Strategies to handle Difficult Customer Situation 3. Respond professionally – Use customer’s name – Maintain friendly manner – Use appropriate body language 4. Recognize underlying factors – Customer act for a reason – Negative emotion
  • 101. Strategies to Handle Difficult Customer Situation 5. Ask question – Be sure to listen to everything 6. Give feedback – Treat the public as customer seeking service – Play tour tone of voice 7. Summarize – Communicate what you will do and when you will do it – Remember to under promise and over deliver
  • 102. Limited English Speaking • Be patient and concentrate – Remember, the customer is just as frustrated as you are • Speak slowly and distinctly – Don’t speak so slowly that it appears to be an insult • Be extra courteous – you really do care and want to help • Avoid using slang or industry jargon – Use plain, simple English. Don’t use terms or phrases that will only add to the confusion • Speak in normal tone of voice – Don’t shout. Speaking loudly won’t help • Don’t try to listen to every word – Listen carefully for key words and phrases
  • 103. Limited English Speaking • Reiterate what has been said – Once the customer has told you what the problem is, summarize • Don’t ask “do you understand?” – The customer may feel you are insulting him or her. • Avoid humor – Stick to the problem. Different cultures view humor in different ways. • Write it down – Use simple, short sentences. • If you speak another language, try using it – The client may understand the other language better than English • Develop a list of employee who speak foreign languages – Use this as a resource for helping non-English speaking customers. • Listen to foreign language tape
  • 104. Tips for Long-Winded Caller • People will monopolize another’s time on the telephone – Don’t think silent or giving short answer will work – don’t ask questions – Refocus the attention • Stating a relevant point – Using “PRC” technique • (Paraphrase, Reflect, Close) – Budget time to listen • Budget what you can afford—but don’t tell the caller you are doing this – Establish mutual time limit • take control of the conversation before it gets too far – Patience: Give extra minute or two • Let the other party go gracefully with statements such as:“I know you are busy. I appreciate your help.” .“Thanks for your time. The information you have provided is very helpful. I’ll be back in touch as soon as. …”
  • 105. Strategies to Handle Argumentative Customer • Speak softly – the customer must be quiet in order to hear you. • Ask for their opinion – If you give them some control by asking a question, they are liable to ease up. • Take a break, don’t get drawn in – excuse yourself briefly, count to 10, or get a drink of water • Concentrate on the points of the argument – Deal with these points one at a time.
  • 106. Strategies to Handle Verbally Abusive Customer • Remember, Customer isn’t angry with you – but at the agency, the situation, or something else completely unrelated • Talk quietly – talk quietly so that he or she has to be quieter to hear you. • Talk at normal pace – If you begin to talk quickly, it will only make matters worse • Let the consumer know the consequences – When you use this language, it makes it impossible for me or anyone to assist you.
  • 107. Strategies to Handle Threatening Customer • Threat can be an attempt to intimidate you • Keep calm and keep your responses focused on the issue at hand
  • 108. Strategies to Handle Threatening Customer • Try to avoid getting into discussion of the threat – Lead the conversation back to the fundamental issue in dispute • Evaluate customer ability to make good on threat and decide what to do from there – Don’t overreact – Look for signs of drug or alcohol use • Advice consumer of the repercussion – Before the threats escalate, calmly advise the customer of the repercussions of the threats, • Terminate the interview – document the threat, warn/alert the appropriate people (supervisor, reception staff, etc.)
  • 109. Strategies to Handle Hostile/Angry Customer 1. An angry customer is most • Don’t likely not angry with you – Take the anger personally – Blame the customer – Avoid blame – Dominate the conversation
  • 110. Strategies to Handle Hostile/Angry Customer 2. Detach yourself from the Customer’s Hostility – Maintain self control 3. Hostility curve – Let’s wait, hear him/her out
  • 111. Strategies to Handle Hostile/Angry • Listen Customer – When the customer stops talking, start giving feedback to indicate you heard his or her key points • Empathize – you understand the situation from the customer’s perspective. Express empathy for the feelings expressed or demonstrated. • Apologize – Apologize when the agency is at fault • Service – S =Say you’re sorry. E = Expedite solutions. R = Respond to the customer. V = Victory to the customer. I = Implement improvements. C = Communicate results. E = Extend the outcome. • Summarize – Clearly communicate what you will do and when you will do it
  • 112. Saying “No” • Sometimes you have to say no, but if you do it right, you can still get a “thank you” for your service
  • 113. Saying “No” • Explain why it cant be done • Don’t quote policy – Don’t say, “Because it’s the law.” • Don’t be patronizing – Don’t talk down to the customer. • Offer alternatives when u can – Try to help the customer find solutions to the problem. • Avoid making excuses – “I’m sorry your case hasn’t been processed yet • Eliminate negative phrases • Don’t mention other/similar complaints
  • 115. Price for Handling Difficult Customer
  • 116. Any Question? • Methods for diffusing the anger and hostility of customers. • Strategies for handling difficult customers
  • 117. Module 6 SUMMARY & WRAP UP

Editor's Notes

  1. Introduce yourself, including your background and training experience. Tell participants how long you have been a trainer. Identify the types of training you have done. Describe any work experiences you have had that relate to this training. Relate an interesting/humorous training experience (this helps to relax participants). Refer participants to the questions listed on the next page. Ask participants to form pairs and conduct five-minute interviews with each other. Participants may write the information on the Training Notes page or on a separate piece of paper. At the end of the interviews, each participant should introduce his/her partner to the class using the information obtained in the interview. Use the flipchart to list participants’ expectations of the course. Tape flipchart pages on the walls. (If some participants state expectations that are not part of this course, refer them to other resources.) As appropriate, affirm that it sounds like they are here for all the right reasons. Display PowerPoint Slide 1-2: Introductions .
  2. Participants will complete a warm-up exercise to become familiar with each other and the trainer. Participant introductions will be conducted as part of this exercise. Team up with an individual you do not know. Take turns asking each other the following questions. It should take approximately five minutes for each set of questions. After you have interviewed each other, you will introduce your partner to the large group. Questions: What is your name? What is your place of employment? How long you have been with Child Support Enforcement and what are your areas of responsibility? What are your expectations of the course? What is one question that you hope to get answered during this class? Tell us one fun thing that you like to do on weekends?
  3. What you need to say/do 1. Display PowerPoint Slide 1-4: Administrative Tasks . 2. Review the training schedule, facility layout, and administrative tasks with participants. 3. Advise participants to be considerate of others and turn off beepers and cell phones. 4. Tell participants that the training manual format in most modules is similar. Ensuring the same “look and feel” from module to module helps the participants to become more familiar and comfortable with the training material. 5. Demonstrate the use of the manual by telling participants to turn to specific page numbers as you describe those sections, modules, and pages.
  4. What you need to say/do Discuss the Training Agenda (on the next page) with participants. Tell participants that the times on the schedule are approximate and intended as a guide. Provide participants with an overview of the objectives for each module as you go over the Training Agenda: Module 1: Introduction : Participants will become familiar with each other and learn course goals and objectives. Module 2: Concepts of High-Quality Customer Service: Participants will identify Child Support Enforcement customers, define effective customer service within the Child Support Enforcement community, identify the most common barriers to providing high-quality customer service and identify and describe the benefits of delivering effective customer service. Module 3: Communication Skills: Participants will identify characteristics of effective listening skills, identify the barriers to active listening, and explain the importance of effective listening in providing high-quality customer service. Module 4: Winning Telephone Techniques : Participants will describe the unique challenges associated with providing effective customer service to telephone callers, and identify the strengths and weaknesses of their telephone styles and techniques. Module 5: Strategies For Handling Difficult Customers: Participants will identify methods for diffusing customer anger or hostility, develop strategies for handling difficult customers, and identify which verbal and nonverbal messages exacerbate a difficult situation and which diffuse a difficult situation. Participants will identify effective barriers, both physical and psychological, which can increase the safety and security of the worker. Module 6: Summary and Wrap–Up: Participants will demonstrate what they have learned in this course.
  5. Module 3: Communication Skills : Identify characteristic of effective Listening skill Identify barrier to active listening Explain the importance of effective listening in providing good cs Module 4: Winning Telephone Techniques Describe the unique challenge for effective cs to callers Identify strength and weaknesses of telephone styles and techniques Module 5: Difficult Customers and Situations Method to diffuse customer anger Develop strategies for handling difficult customer Identify verbal and non verbal massages Identify effective barriers Module 6 : Course Summary and Wrap Up 1. Participants will demonstrate what they have learned in this course
  6. At this point, it may be useful to point out the “time horizons” considered by different industries. For example, some colleges and universities look 30 to fifty years ahead, industries engaged in long distance transportation (steam ship, railroad) or provision of basic power (electrical and gas utilities, etc.) also look far ahead (20 to 100 years). Ask them to give examples of industries having much shorter long-range horizons.
  7. At this point it may be helpful to discuss the actual variables one might wish to forecast in the various time periods.
  8. This slide introduces the impact of product life cycle on forecasting The following slide, reproduced from chapter 2, summarizes the changing issues over the product’s lifetime for those faculty who wish to treat the issue in greater depth.
  9. Introduce yourself, including your background and training experience. Tell participants how long you have been a trainer. Identify the types of training you have done. Describe any work experiences you have had that relate to this training. Relate an interesting/humorous training experience (this helps to relax participants). Refer participants to the questions listed on the next page. Ask participants to form pairs and conduct five-minute interviews with each other. Participants may write the information on the Training Notes page or on a separate piece of paper. At the end of the interviews, each participant should introduce his/her partner to the class using the information obtained in the interview. Use the flipchart to list participants’ expectations of the course. Tape flipchart pages on the walls. (If some participants state expectations that are not part of this course, refer them to other resources.) As appropriate, affirm that it sounds like they are here for all the right reasons. Display PowerPoint Slide 1-2: Introductions .
  10. One can use an example based upon one’s college or university. Students can be asked why each of these forecast types is important to the college. Once they begin to appreciate the importance, one can then begin to discuss the problems. For example, is predicting “demand” merely as simple as predicting the number of students who will graduate from high school next year (i.e., a simple counting exercise)?
  11. A point to be made here is that one requires a forecasting “plan,” not merely the selection of a particular forecasting methodology.
  12. This slide illustrates a typical demand curve. You might ask students why it is important to know more than simply the actual demand over time. Why, for example, would one wish to be able to break out a “seasonality” factor?
  13. This slide illustrates one of the simplest forecasting techniques - the moving average. It may be useful to point out the lag introduced by exponential smoothing - and ask how one can actually make use of the forecast.
  14. This slide provides a framework for discussing some of the inherent difficulties in developing reliable forecasts. You may wish to include in this discussion the difficulties posed by attempting forecast in a continuously, and rapidly changing environment where product life-times are measured less often in years and more often in months than ever before. One might wish to emphasize the inherent difficulties in developing reliable forecasts.
  15. This slide outlines several qualitative methods of forecasting. Ask students to give examples of occasions when each might be appropriate. The next several slides elaborate on these qualitative methods.
  16. Ask your students to consider other potential disadvantages. (Politics?)
  17. You might ask your students to consider what problems might occur when trying to use this method to predict sales of a potential new product.
  18. You might ask your students to consider whether there are special examples where this technique is required. ( Questions of technology transfer or assessment, for example; or other questions where information from many different disciplines is required.)
  19. You might discuss some of the difficulties with this technique. Certainly there is the issue that what consumers say is often not what they do. There are other problems such as that consumers sometime wish to please the surveyor; and for unusual, future, products, consumers may have a very imperfect frame of reference within which to consider the question.
  20. This and subsequent slide frame a discussion on time series - and introduce the various components.
  21. What you need to say/do Remind participants that in Module 1 we talked briefly about how perception, rather than facts and events, shapes a customer’s opinion of the quality of customer service he or she has received. In this module, we will explore the true meaning of high-quality customer service. Display PowerPoint Slide 2-1: Concepts of High-Quality Customer Service .
  22. This slide introduces two general forms of time series model. You might provide examples of when one or the other is most appropriate.
  23. This slide introduces the naïve approach. Subsequent slides introduce other methodologies.
  24. At this point, you might discuss the impact of the number of periods included in the calculation. The more periods you include, the closer you come to the overall average; the fewer, the closer you come to the value in the previous period. What is the tradeoff?
  25. This slide shows the resulting forecast. Students might be asked to comment on the useful ness of this forecast.
  26. This slide introduces the “weighted moving average” method. It is probably most important to discuss choice of the weights.
  27. This slide illustrates one of the simplest forecasting techniques - the moving average. It may be useful to point out the lag introduced by exponential smoothing - and ask how one can actually make use of the forecast.
  28. The learning objectives for this module are: Given a participative lecture, participants will 1. identify characteristics of effective listening skills. 2. Through a group activity and a large group discussion, participants will identify barriers to active listening. 3. Through a group activity and given a participative lecture, participants will explain the importance of effective listening in providing high-quality customer service. 4. Through an individual activity and participative lecture, participants will identify the strengths and weaknesses in their listening styles.
  29. These points should have been brought out in the example, but can be summarized here.
  30. This slide introduces the exponential smoothing method of time series forecasting. The following slide contains the equations, and an example follows.
  31. You may wish to discuss several points: - this is just a moving average wherein every point in included in the forecast, but the weights of the points continuously decrease as they extend further back in time. - the equation actually used to calculate the forecast is convenient for programming on the computer since it requires as data only the actual and forecast values from the previous time point. - we need a formal process and criteria for choosing the “best” smoothing constant.
  32. This slide begins an exponential smoothing example.
  33. This slide illustrates the result of the steps used to make the forecast desired in the example. In the PowerPoint presentation, there are additional slides to illustrate the individual steps.
  34. This slide illustrates graphically the results of the example forecast.
  35. This slide provides a quick view of the development of a multiplicative seasonal model.
  36. What you need to say/do Remind participants that in Module 1 we talked briefly about how perception, rather than facts and events, shapes a customer’s opinion of the quality of customer service he or she has received. In this module, we will explore the true meaning of high-quality customer service. Display PowerPoint Slide 2-1: Concepts of High-Quality Customer Service .
  37. This slide introduces the linear regression model. This can be approached as simply a generalization of the linear trend model where the variable is something other than time and the values do not necessarily occur a t equal intervals.
  38. This slide probably merits discussion - additional to that for the linear trend model. You might make the point here that the dependent and independent variable are not necessarily of the same nature - they need not both be dollars, for example. You might also wish to note that setting x = 0 may not have a useful physical interpretation.
  39. Here you may wish to at least begin the discussion of the distinction between explainable and unexplainable, and random and non-random error variation. There are also slides which come later in the presentation that will refer to this topic.
  40. This slide raises several points: - What does it mean to be “linear”? How does one tell if something is linear or not? Or perhaps, how does one tell if something is sufficiently linear that a linear regression model is appropriate? - If the relationship is assumed to hold only within or slightly outside the data range, how do we use this model to make projections into the future (for which we don’t have data)? - What does it mean for data to be random? How can we tell? You might discuss making scatter plots not only of the original data, but also of the resulting deviations. (Obviously there are more rigorous methods of determining if the deviations are random, but a scatter plot is a good start.)
  41. Again, it is probably useful to point out which elements in the equations represent the actual data values and which the averages of these values.
  42. This slide can frame the start of a discussion of correlation.. You should probably expect to add to this a discussion of cause and effect, emphasizing in particular that correlation does not imply a cause and effect relationship. Ask student to suggest examples of significant correlation of unrelated phenomenon.
  43. What you need to say/do Remind participants that in Module 1 we talked briefly about how perception, rather than facts and events, shapes a customer’s opinion of the quality of customer service he or she has received. In this module, we will explore the true meaning of high-quality customer service. Display PowerPoint Slide 2-1: Concepts of High-Quality Customer Service .
  44. Here again an explanation of each variable is probably useful.
  45. While this slide introduces the implications of negative and positive correlation, it is probably also a good point to re-emphasis the difference between correlation and cause and effect.
  46. This slide introduces overall guideline for selecting a forecasting model. You may also wish to re-emphasize the role of scatter plots, and discuss the role of “understanding what is going on” (especially in limiting one’s choice of model).
  47. This slide illustrates both possible patterns in forecast error, and the merit of making a scatter plot of forecast error.
  48. What you need to say/do Remind participants that in Module 1 we talked briefly about how perception, rather than facts and events, shapes a customer’s opinion of the quality of customer service he or she has received. In this module, we will explore the true meaning of high-quality customer service. Display PowerPoint Slide 2-1: Concepts of High-Quality Customer Service .
  49. What you need to say/do Tell participants to take a few minutes and complete the Assessment Tool. Advise them that this will not be collected. This is purely a tool to determine their strengths and weaknesses in the customer service area. Once participants are done with the tool, let them know that each item is addressed in the various modules we will be covering throughout this course. It is critical to your work to have a self-awareness of the areas on which you need to work as we go through this course. Briefly highlight the following in the Handout 2-1, Customer Service Self-Assessment Tool: Job knowledge. We will talk more about how critical this is in Module 2. Follow-up. If you promise to call within a certain timeframe, do it. We will talk about a concept called “Under-promise, over-deliver” later in this module. A prevalent complaint about the company product is not following through, or not following through, or not following through in the timeframe promised. Customer sensitivity. We will talk about how important it is to focus on what the customer is feeling, and what he may not be saying. Decisiveness. Never, never say, “That’s not my job.” Impact. We will talk in this module, Module 2, and in Module 3 about how much of an impact body language plays in communication. Initiative. “Go the extra mile.” We will talk more about this concept later in this module. Strive to take action beyond what is being called for by the customer.
  50. What you need to say/do In Module 3: Communication Skills , we talked about good communication skills and how to work with barriers to effective communication in person. Now, we will explore the most effective ways to communicate by phone. Tell participants that we will explore how each of us has the ability to make positive—or negative—impressions on the people we talk to on the telephone. In most cases, the impression you give callers influences how they feel about your agency as a whole. To anyone who contacts your agency—in person or by phone— you are the agency . (Write this on a flipchart.) Remind participants that every person they communicate with is a customer and every communication makes an impression. What kind of impression will you make today and every day? Remind participants that if you create a good first impression, the relationship grows from there. Create a bad first impression and your relationship with that customer can be an uphill battle. Many customers will call before ever visiting the company, and what they encounter over the phone is critical to creating a positive image. Display PowerPoint Slide 4-1: Winning Telephone Techniques .
  51. What you need to say/do Display PowerPoint Slides 2-19: Summary & Conclusions . Summarize the key points made in this module. Ask participants if they believe that the goal and objectives of this module have been met. Tell participants that we have now completed Module 2, Concepts of High-Quality Customer Service . Ask if there are any questions. After answering the participants’ questions, transition to Module 3: Communication Skills . What you need to know These were the learning goals and objectives of this module: Given a participative lecture, participants will identify Child Support Enforcement community customers. Through a group activity and in a large group discussion, participants will define effective customer service within the Child Support Enforcement community. Given a participative lecture, participants will identify the most common barriers to providing high-quality customer service. Given a participative lecture, participants will define “self-talk” and provide both positive and negative examples. Through a large group discussion, participants will identify and describe the benefits of delivering effective customer service.
  52. What you need to say/do Display PowerPoint Slide 5-4: Difficult Customer Situations . Tell participants that first we will cover some strategies to use overall with difficult customers and/or situations, and then we will look at some specific examples. Tell participants that in dealing with difficult customers and/or situations, we need to incorporate all that we have discussed in Modules 1 through 4. We will now look at some more specific strategies. Remind participants of our earlier discussions about listening. It is important to take the time to use active listening skills. Discuss the fine line between being empathetic and being a therapist. You cannot solve all problems. Remind participants that we discussed what empathy means earlier: It means putting yourself in the customer’s shoes, letting him or her know that you understand the situation and how the customer feels. Remind participants that it is important not to stray from their area of professional expertise by offering ways to deal with the emotional or other problems that are not directly related to child support. Example : It would not be appropriate to address how to relieve depression, stress, or anxiety that the customer may tell you about or display. Understanding the customer’s concerns is important, but “treating” that feeling or giving advice may be crossing a fine line. Example: Should I sue for custody? Should I keep the kids longer? Should I ask for an increase in support? The agency should stick with what they can do, not with what a customer “should” do. Discuss the issues regarding giving “advice” and emphasize that participants should not offer legal advice. Workers can get into real trouble if the client gets the impression that they have an attorney-client relationship. This is one very good reason to treat the public as customers seeking service, rather than as clients seeking advice and direction. Tell participants that empathetic phrases are simple and easy ways to convey that you understand your customer’s situation. Remember empathy. See the world from your customer’s side of the desk/phone. On a flip chart using the title “Examples of Empathetic Phrases,” list the examples below. What you need to know Some examples of empathetic phrases participants can use: I can see why you feel that way. I see what you mean. That must be very upsetting. I understand how frustrating this must be. I’m sorry about this.
  53. To provide effective customer service—especially in difficult situations—we need to deal with the customer’s emotions first, then the problem. When dealing with difficult customers and situations, it is important to use the following strategies. Listen Use active and reflective listening skills. Empathize Empathy means putting yourself in the customer’s shoes, letting him or her know that you understand not only the situation, but also how the situation makes the customer feel. When we empathize, we connect with the person’s feelings in two ways: (1) by making a statement that tells the person we understand the feeling, and (2) by paraphrasing his or her words to show the person we understand the issue, while not necessarily agreeing with him or her. You can get into trouble if it seems to the client that you are offering legal advice. Stick to what the agency can and can’t do—and let the client seek an attorney if he or she is not sure what he or she should do.
  54. What you need to say/do Display PowerPoint Slide 5-5: Difficult Customer Situations . It is important to be professional when using someone’s name. Never use the customer’s first name, unless advised to do so. In “How to Win Friends and Influence People,” Dale Carnegie wrote, “A man’s name is to him the sweetest and most important sound in any language.” Remembering anyone’s name, for man or woman, is a compliment. This can go a long way toward diffusing anger. Remind participants of our earlier discussions regarding body language. Body language can be a critical factor when one is involved in a difficult situation. It can either exacerbate that situation or create a calming influence. Ask participants if they can name any other negative emotions. Remind participants of the feelings that were evoked when we did the exercise with the Talker, Listener, and Critiquer. One of the feelings evoked may have been hurt at not being listened to. Many people don’t show that they are hurt, but mask it with other emotions such as anger or hostility. Tell participants that the most effective way to handle a negative conflict situation is to listen. Only when people feel that they have been heard will they be ready to hear what we have to say. Tell participants that we need to remember: Customers are not always saints, nor are they always right. But they are always customers and it’s our job to provide courteous and professional service. Remind participants of the listening exercise we completed in Module 3 and how using good listening skills may help with negative emotions. Respond Professionally Don’t take the anger personally. As a professional, recognize that customers may have legitimate concerns buried somewhere in their anger and venting. They may be overreacting, but you need to remain objective, assess the problem, and focus on solutions. Whenever possible, use the customer’s name . This personalizes the conversation and makes it difficult for the customer to attack you. Maintain a friendly manner . Show the customer respect, even in the face of disrespect. Demonstrate no reaction in the face of difficult behavior. Use appropriate body language . Move closer to the customer and maintain eye contact. Listen for the unspoken message. Focus on subtleties in a caller’s voice—inflection, pacing, and the overall tension level. Recognize Underlying Factors Customers may act angry, upset, demanding, impatient, abusive, and threatening for any number of reasons. These behaviors occur as a result of one or more negative feelings that have been aroused in the situation. Negative emotions, such as: I’m frustrated I’m powerless and a victim I’m not important I’m stupid I’m incompetent I’m guilty
  55. Ask questions As you ask the customer questions, be sure to listen to everything he or she says and don’t jump to conclusions. You might miss details that are specific to this customer’s situation. Give feedback Treat the public as customers seeking service, rather than as clients seeking advice and direction. The tone of your voice goes a long way toward helping you convey empathy. If you say all the right words, but deliver them with coldness in your voice, you will sound insincere. Summarize Clearly communicate what you will do and when you will do it. Reach a full understanding of what you will do and what the customer needs to do. Discuss any reasonable future contingencies and what can be done about them. Determine to follow up! Remember to under-promise and over-deliver.
  56. Strategy Be patient and concentrate . Remember, the customer is just as frustrated as you are. If you are patient and concentrate on the conversation, you will be better able to understand what the customer is saying. Speak slowly and distinctly . Don’t speak so slowly that it appears to be an insult, but speak slowly enough that the customer can follow what you are saying. Also, if you speak slowly, the customer will do the same. Be extra courteous . This shows that you really do care and want to help. It allows customers to relax and eases their frustrations. Avoid using slang or industry jargon . Use plain, simple English. Don’t use terms or phrases that will only add to the confusion. Speak in a normal tone of voice . Don’t shout. Speaking loudly won’t help—it will probably only cause more anxiety. And if you speak loudly, the customer will speak loudly. Don’t try to listen to every word . Listen carefully for key words and phrases.
  57. Everyone loves an audience, and, because it’s rare to find someone who will listen, some people will monopolize another’s time on the telephone as a break from their hectic day or just to combat loneliness. Most of these people don’t realize how they inconvenience others. You need strategies to deal effectively with the “rambling” caller. Strategy Don’t think silence or giving short answers will work , under the assumption the caller will “get the hint.” On the phone, silence is like a vacuum—it demands to be filled. If you don’t respond, he or she will keep talking. Do ask questions . Don’t be afraid to interrupt the rambler with a question. They won’t be offended as long as you appear interested in their response. Use their responses to begin moving toward a conclusion. Set the course of the conversation , using statements such as: “ Mr. Smith, I need to ask you three questions concerning. …” “ I understand you are having trouble understanding your billing statement. Let me take a few minutes to explain it.” Refocus the attention by stating a relevant point Use the “PRC” technique : P araphrase, R eflect, C lose Paraphrase: “I need to make sure I understand what you’ve said.” Emphasize the key points and then shift to addressing just these points. Reflect: Allow the caller to argue, disagree, or add to what you just said. Close: Express appreciation for the caller’s time, mention any action you agreed on, and then end the call. Budget time to listen . Callers often ramble because they are lonely and need someone to talk to. When you talk to customers, you have two conflicting desires: To create a positive image of your agency and to get off the phone in a reasonable time frame. You can do both by investing a specific amount of time listening. Budget what you can afford—but don’t tell the caller you are doing this! As the end of the budgeted time approaches, segue to the subject at hand, interrupt with a question, or give the caller feedback to show that you heard him or her, and then get on to business, or wrap up the call. Establish mutual time limits . When you pick up the phone and realize you have a rambler, take control of the conversation before it gets too far. “Mr. Brown, I need to be in a meeting in 5 minutes. Can we cover what you need now, or can I call you back?” Patience: Give the extra minute or two . To protect your agency’s reputation and image, use a good technique for closure, rather than being abrupt or rude. Seek a smooth transition. Summarize the conversation. Repeat action steps on which you agreed so both parties know what they are responsible for, and what comes next. Let the other party go gracefully with statements such as: “ I know you are busy. I appreciate your help.” “ Thanks for your time. The information you have provided is very helpful. I’ll be back in touch as soon as. …”
  58. Some people thrive on arguments. They are aggressive and probably will disagree with or question everything you say or propose. Your first instinct may be to argue back. Don’t fall into this trap. Strategy Speak softly . If you speak loudly, then the customer needs to speak loudly to be heard over you, and then you speak louder—and before you know it, you’ll be shouting at each other. Speak softly so the customer must be quiet in order to hear you. Ask for their opinions . Argumentative people like to feel they are in control. If you try to rob them of their control, they become more argumentative. If you give them some control by asking a question, they are liable to ease up. Take a break . If you allow yourself to be drawn into the argument and become angry, excuse yourself briefly, count to 10, or get a drink of water. Allow yourself a minute or two to regain your composure. Concentrate on the points of the argument and list them for both of you to see. Deal with these points one at a time. Take notes on the points of the argument. Number each problem so that it can be addressed.
  59. Strategy Remember, the customer isn’t angry at you . The customer isn’t angry with you personally, but at the agency, the situation, or something else completely unrelated. Talk quietly . If the customer is yelling, talk quietly so that he or she has to be quieter to hear you. Talk at a normal pace . If you begin to talk quickly, it will only make matters worse. Be direct . If the customer uses abusive language or makes threats, be direct. Address the client by name and say, for example, “Mr. Smith, I understand that you are upset, but do not use that language/threaten me.” Let the customer know the consequences, calmly and objectively . “When you use this language, it makes it impossible for me or anyone to assist you. We’ll have to reschedule your appointment/postpone the resolution of this problem until we can talk about it rationally.”
  60. Strategy Try to avoid getting into a discussion of the threat . Lead the conversation back to the fundamental issue in dispute. Remind the customer that you are equally interested in finding an equitable solution. Offer to get a third party involved who can evaluate the problem and options. Admit that someone else might have another option that the two of you haven’t come up with. Evaluate the customer’s ability to make good on the threat and decide what to do from there . Don’t overreact; however, there may be occasions when you fear, deep down, for your safety. Look for signs of drug or alcohol use—they may impair the customer’s ability to be rational, and may necessitate taking steps to ensure your personal safety. Advise the customer of the repercussions . Before the threats escalate, calmly advise the customer of the repercussions of the threats, of the fact that threats are taken seriously and treated seriously, and suggest that the customer may want to reconsider. Terminate the interview . If the customer continues the threats, terminate the interview, document the threat, warn/alert the appropriate people (supervisor, reception staff, etc.), and, if necessary, contact the police.
  61. Strategy Detach Yourself from the Customer’s Hostility . Remain professional. Don’t be defensive. Maintain control of yourself and the situation by viewing it objectively. Don’t listen to the personal attacks, untruths, etc. Hostility Curve Let the customer vent. The fastest way to diffuse a customer’s anger is to let him or her blow off steam. Don’t interrupt. Remember, it takes two to sustain a conflict. If you begin responding to the customer’s points while he or she is venting, the customer has engaged you in the argument. If you respond, it will be seen as a rebuttal. The customer will think you disagree, and the situation will escalate. Wait. Hear him or her out. Sometimes it seems that letting the customer vent takes too much time. What is your alternative? Until the customer gets through his or her anger, he or she won’t be able to listen or work toward solutions. Try to listen for and focus on the real problem. Don’t say anything during the customer’s “venting” -- except maybe “I see,” or “I understand”-- to let him know you are attentive. Let the customer vent until you hear silence.
  62. Strategy Detach Yourself from the Customer’s Hostility . Remain professional. Don’t be defensive. Maintain control of yourself and the situation by viewing it objectively. Don’t listen to the personal attacks, untruths, etc. Hostility Curve Let the customer vent. The fastest way to diffuse a customer’s anger is to let him or her blow off steam. Don’t interrupt. Remember, it takes two to sustain a conflict. If you begin responding to the customer’s points while he or she is venting, the customer has engaged you in the argument. If you respond, it will be seen as a rebuttal. The customer will think you disagree, and the situation will escalate. Wait. Hear him or her out. Sometimes it seems that letting the customer vent takes too much time. What is your alternative? Until the customer gets through his or her anger, he or she won’t be able to listen or work toward solutions. Try to listen for and focus on the real problem. Don’t say anything during the customer’s “venting” -- except maybe “I see,” or “I understand”-- to let him know you are attentive. Let the customer vent until you hear silence. Listen – Give Feedback When the customer stops talking, start giving feedback to indicate you heard his or her key points. Don’t agree or disagree, just summarize. Ask questions to verify the facts. Empathize Communicate that you understand the situation from the customer’s perspective. Express empathy for the feelings expressed or demonstrated. Apologize Apologize when the agency is at fault. Express regret when something happened over which your agency has no control. Apology: “I’m sorry we neglected to mail you your statement.” Regret: “It’s unfortunate that the weather conditions resulted in our office closing yesterday, which is why we needed to reschedule your appointment.” SERVICE S =Say you’re sorry. E = Expedite solutions. R = Respond to the customer. V = Victory to the customer. I = Implement improvements. C = Communicate results. E = Extend the outcome. Summarize . Clearly communicate what you will do and when you will do it. Reach a full understanding of what you will do and what the customer needs to do. Talk about any reasonable future contingencies, and what can be done about them. Determine to follow up! Close positively . Express confidence in a positive resolution. Thank the customer for working with you to resolve the problem. Don’t let the angry customer ruin the rest of your day . If you do, it will make it more difficult to deal with subsequent customers and it may affect your overall attitude toward the public, your job, your boss, your agency, your co-workers, etc. Hanging onto the anger also reduces your efficiency. When you hang up the phone after an angry call or leave an unpleasant face-to-face interview, remember the saying “out of sight, out of mind.” When a customer says goodbye (or hangs up on you in anger) he or she is gone. You can go on to the next caller or next task and leave the previous caller’s anger behind.
  63. Work for a “thank you.” Sometimes—often—you have to say no, but if you do it right, you can still get a “thank you” for your service. Strategy Explain why it can’t be done . Give details, but concentrate on the positive and don’t dwell on the negative. Instead of saying “I can’t help you,” say, “We can’t do that, but we can do this.” Don’t quote policy . Don’t say, “Because it’s the law.” Give the customer some background and some explanation. Don’t be patronizing . Don’t talk down to the customer. Keep comments on a professional, adult level. Don’t use the phrase “of course.” (Example: “Of course you don’t understand. You didn’t read my letter.” ) It sounds patronizing and sarcastic. Offer alternatives when you can . Don’t just say no, or “You have to.” Try to help the customer find solutions to the problem. “I apologize for not being able to find the form and having to ask you to fill it out again. Would it be easier for you to come in and do it, or should I drop it into the mail for you?” Avoid making excuses . Instead of saying “I’m sorry your case hasn’t been processed yet, but everyone has been on vacation and we’re pretty backlogged,” say, “I’m sorry your case hasn’t been processed yet. Let’s see how we can expedite the matter, and what you can expect in the future.” Eliminate negative phrases such as “You have to.” Instead of “I can’t do that. You have to talk to Bob,” say, “Let me see if I can transfer you to Bob, who is the one who can make that decision.” Don’t mention other/similar complaints . “You know, a lot of people don’t like that law,” or “You know, our computer has been doing that a lot lately.”
  64. What you need to say/do Display PowerPoint Slide 5-18: Summary & Conclusions . Summarize the material that we have covered in this module. Solicit feedback from participants for each bullet on the participant page. Ask if there are any further questions about this module. After answering the participants’ questions, you can transition to the final module, Module 6: Summary and Wrap-Up . Allow a few minutes for participants to look at Handout 1-1, the Customer Service Training Evaluation Form and evaluate Module 5 at this time (or remind them to do so at the end of the course). What you need to know The learning objectives for this module are: Given a participative lecture, participants will identify methods for diffusing customer anger or hostility. Given a participative lecture and small group activity, participants will develop strategies for handling difficult customers. Given a participative lecture, participants will identify which verbal and nonverbal messages exacerbate a difficult situation and which diffuse a difficult situation. Throughout this module, or at the end, the trainer should go to the flipchart and check off any sticky notes that have been addressed.
  65. Remind participants of our discussion in Module 2 on what effective customer service is within the Child Support Enforcement Program community. Refer back to the Effective C/S flipchart displayed on the wall. Ask participants for feedback to see if these definitions need revisiting or revising. We have identified who our customers are and those we serve, and have emphasized the importance of maintaining high quality customer service. Keep in mind that certain strategies will help you in providing positive customer experience, such as: staying professional, unemotional; practicing positive self-talk; limiting the use of jargon or legal terms; helping the customer focus on the problem; and empathizing with the person. Questions: Who are our customers? Name 3 (CPs, NCPs, Dependents, Employers, Courts, other agencies, IV-A, Sheriff Dept., etc.) Name 2 effective customer service techniques (ask two different people). Which is not a benefit of Effective Customer Service; a) decrease in calls/walk-ins; b) increase in productivity; c) increase in complaints; d) improved reputation.   What you need to know Give prizes, candy and one toy for harder questions. Throughout this module, or at the end, the trainer should go to the flipchart and make